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Fix deprecated langchain warning for llama3 #953

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Merged
merged 5 commits into from
Jun 3, 2025
Merged

Fix deprecated langchain warning for llama3 #953

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fa38b37
Fix deprecated langchain warning for llama3
May 25, 2025
f251c3b
Update: addressing comments + fixing hugging face import
May 28, 2025
ad91da4
nit - fixing last cell
May 28, 2025
571c247
nit - cleanup
May 28, 2025
319b954
add beautifulsoup4 to dependency
IgorKasianenko May 29, 2025
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104 changes: 52 additions & 52 deletions 3p-integrations/langchain/langgraph_rag_agent_local.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,7 @@
"metadata": {},
"outputs": [],
"source": [
"! pip install -U langchain_community tiktoken langchainhub chromadb langchain langgraph tavily-python sentence-transformers"
"! pip install -U langchain-huggingface langchain_community langchain-ollama tiktoken langchainhub chromadb langchain langgraph tavily-python sentence-transformers beautifulsoup4"
]
},
{
Expand Down Expand Up @@ -99,7 +99,8 @@
"os.environ['LANGCHAIN_ENDPOINT'] = 'https://api.smith.langchain.com'\n",
"os.environ['LANGCHAIN_API_KEY'] = 'LANGCHAIN_API_KEY'\n",
"\n",
"os.environ['TAVILY_API_KEY'] = 'TAVILY_API_KEY'"
"os.environ['TAVILY_API_KEY'] = 'TAVILY_API_KEY'\n",
"os.environ[\"USER_AGENT\"] = \"USER_AGENT_KEY\""
]
},
{
Expand All @@ -126,7 +127,7 @@
"from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
"from langchain_community.document_loaders import WebBaseLoader\n",
"from langchain_community.vectorstores import Chroma\n",
"from langchain_community.embeddings import HuggingFaceEmbeddings\n",
"from langchain_huggingface import HuggingFaceEmbeddings\n",
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I have created brand new conda env to test it end to end and I am getting

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[4], line 14
      6 from langchain_huggingface import HuggingFaceEmbeddings
      8 urls = [
      9     "https://lilianweng.github.io/posts/2023-06-23-agent/",
     10     "https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/",
     11     "https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/",
     12 ]
---> 14 docs = [WebBaseLoader(url).load() for url in urls]
     15 docs_list = [item for sublist in docs for item in sublist]
     17 text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
     18     chunk_size=250, chunk_overlap=0
     19 )

Cell In[4], line 14, in <listcomp>(.0)
      6 from langchain_huggingface import HuggingFaceEmbeddings
      8 urls = [
      9     "https://lilianweng.github.io/posts/2023-06-23-agent/",
     10     "https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/",
     11     "https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/",
     12 ]
---> 14 docs = [WebBaseLoader(url).load() for url in urls]
     15 docs_list = [item for sublist in docs for item in sublist]
     17 text_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
...
--> 347     from bs4 import BeautifulSoup
    349     if parser is None:
    350         if url.endswith(".xml"):

ModuleNotFoundError: No module named 'bs4'

"\n",
"urls = [\n",
" \"https://lilianweng.github.io/posts/2023-06-23-agent/\",\n",
Expand All @@ -146,7 +147,7 @@
"vectorstore = Chroma.from_documents(\n",
" documents=doc_splits,\n",
" collection_name=\"rag-chroma\",\n",
" embedding=HuggingFaceEmbeddings(),\n",
" embedding=HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\"),\n",
")\n",
"retriever = vectorstore.as_retriever()"
]
Expand All @@ -158,27 +159,27 @@
"metadata": {},
"outputs": [],
"source": [
"### Retrieval Grader \n",
"### Retrieval Grader\n",
"\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain_community.chat_models import ChatOllama\n",
"from langchain_ollama import ChatOllama\n",
"from langchain_core.output_parsers import JsonOutputParser\n",
"\n",
"# LLM\n",
"llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
"\n",
"prompt = PromptTemplate(\n",
" template=\"\"\"You are a grader assessing relevance \n",
" of a retrieved document to a user question. If the document contains keywords related to the user question, \n",
" grade it as relevant. It does not need to be a stringent test. The goal is to filter out erroneous retrievals. \n",
" \n",
" template=\"\"\"You are a grader assessing relevance\n",
" of a retrieved document to a user question. If the document contains keywords related to the user question,\n",
" grade it as relevant. It does not need to be a stringent test. The goal is to filter out erroneous retrievals.\n",
"\n",
" Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.\n",
" Provide the binary score as a JSON with a single key 'score' and no premable or explanation.\n",
" \n",
" Here is the retrieved document: \n",
"\n",
" Here is the retrieved document:\n",
" {document}\n",
" \n",
" Here is the user question: \n",
"\n",
" Here is the user question:\n",
" {question}\n",
" \"\"\",\n",
" input_variables=[\"question\", \"document\"],\n",
Expand Down Expand Up @@ -206,12 +207,12 @@
"\n",
"# Prompt\n",
"prompt = PromptTemplate(\n",
" template=\"\"\"You are an assistant for question-answering tasks. \n",
" Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. \n",
" template=\"\"\"You are an assistant for question-answering tasks.\n",
" Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know.\n",
" Use three sentences maximum and keep the answer concise:\n",
" Question: {question} \n",
" Context: {context} \n",
" Answer: \n",
" Question: {question}\n",
" Context: {context}\n",
" Answer:\n",
" \"\"\",\n",
" input_variables=[\"question\", \"document\"],\n",
")\n",
Expand Down Expand Up @@ -239,25 +240,25 @@
"metadata": {},
"outputs": [],
"source": [
"### Hallucination Grader \n",
"### Hallucination Grader\n",
"\n",
"# LLM\n",
"llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
"\n",
"# Prompt\n",
"prompt = PromptTemplate(\n",
" template=\"\"\"You are a grader assessing whether \n",
" an answer is grounded in / supported by a set of facts. Give a binary score 'yes' or 'no' score to indicate \n",
" whether the answer is grounded in / supported by a set of facts. Provide the binary score as a JSON with a \n",
" template=\"\"\"You are a grader assessing whether\n",
" an answer is grounded in / supported by a set of facts. Give a binary score 'yes' or 'no' score to indicate\n",
" whether the answer is grounded in / supported by a set of facts. Provide the binary score as a JSON with a\n",
" single key 'score' and no preamble or explanation.\n",
" \n",
"\n",
" Here are the facts:\n",
" {documents} \n",
" {documents}\n",
"\n",
" Here is the answer: \n",
" Here is the answer:\n",
" {generation}\n",
" \"\"\",\n",
" input_variables=[\"generation\", \"documents\"],\n",
" input_variables=[\"generation\", \"documents\"]\n",
")\n",
"\n",
"hallucination_grader = prompt | llm | JsonOutputParser()\n",
Expand All @@ -271,19 +272,19 @@
"metadata": {},
"outputs": [],
"source": [
"### Answer Grader \n",
"### Answer Grader\n",
"\n",
"# LLM\n",
"llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
"\n",
"# Prompt\n",
"prompt = PromptTemplate(\n",
" template=\"\"\"You are a grader assessing whether an \n",
" answer is useful to resolve a question. Give a binary score 'yes' or 'no' to indicate whether the answer is \n",
" template=\"\"\"You are a grader assessing whether an\n",
" answer is useful to resolve a question. Give a binary score 'yes' or 'no' to indicate whether the answer is\n",
" useful to resolve a question. Provide the binary score as a JSON with a single key 'score' and no preamble or explanation.\n",
" \n",
"\n",
" Here is the answer:\n",
" {generation} \n",
" {generation}\n",
"\n",
" Here is the question: {question}\n",
" \"\"\",\n",
Expand All @@ -304,28 +305,28 @@
"### Router\n",
"\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain_community.chat_models import ChatOllama\n",
"from langchain_ollama import ChatOllama\n",
"from langchain_core.output_parsers import JsonOutputParser\n",
"\n",
"# LLM\n",
"llm = ChatOllama(model=local_llm, format=\"json\", temperature=0)\n",
"\n",
"prompt = PromptTemplate(\n",
" template=\"\"\"You are an expert at routing a \n",
" user question to a vectorstore or web search. Use the vectorstore for questions on LLM agents, \n",
" prompt engineering, and adversarial attacks. You do not need to be stringent with the keywords \n",
" in the question related to these topics. Otherwise, use web-search. Give a binary choice 'web_search' \n",
" or 'vectorstore' based on the question. Return the a JSON with a single key 'datasource' and \n",
" no premable or explanation. \n",
" \n",
" Question to route: \n",
" template=\"\"\"You are an expert at routing a\n",
" user question to a vectorstore or web search. Use the vectorstore for questions on LLM agents,\n",
" prompt engineering, and adversarial attacks. You do not need to be stringent with the keywords\n",
" in the question related to these topics. Otherwise, use web-search. Give a binary choice 'web_search'\n",
" or 'vectorstore' based on the question. Return the a JSON with a single key 'datasource' and\n",
" no premable or explanation.\n",
"\n",
" Question to route:\n",
" {question}\"\"\",\n",
" input_variables=[\"question\"],\n",
")\n",
"\n",
"question_router = prompt | llm | JsonOutputParser()\n",
"question = \"llm agent memory\"\n",
"docs = retriever.get_relevant_documents(question)\n",
"docs = retriever.invoke(question)\n",
"doc_txt = docs[1].page_content\n",
"print(question_router.invoke({\"question\": question}))"
]
Expand Down Expand Up @@ -371,7 +372,7 @@
" question: question\n",
" generation: LLM generation\n",
" web_search: whether to add search\n",
" documents: list of documents \n",
" documents: list of documents\n",
" \"\"\"\n",
" question : str\n",
" generation : str\n",
Expand Down Expand Up @@ -412,7 +413,7 @@
" print(\"---GENERATE---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
" \n",
"\n",
" # RAG generation\n",
" generation = rag_chain.invoke({\"context\": documents, \"question\": question})\n",
" return {\"documents\": documents, \"question\": question, \"generation\": generation}\n",
Expand All @@ -432,7 +433,7 @@
" print(\"---CHECK DOCUMENT RELEVANCE TO QUESTION---\")\n",
" question = state[\"question\"]\n",
" documents = state[\"documents\"]\n",
" \n",
"\n",
" # Score each doc\n",
" filtered_docs = []\n",
" web_search = \"No\"\n",
Expand All @@ -451,7 +452,7 @@
" web_search = \"Yes\"\n",
" continue\n",
" return {\"documents\": filtered_docs, \"question\": question, \"web_search\": web_search}\n",
" \n",
"\n",
"def web_search(state):\n",
" \"\"\"\n",
" Web search based based on the question\n",
Expand Down Expand Up @@ -493,7 +494,7 @@
" print(\"---ROUTE QUESTION---\")\n",
" question = state[\"question\"]\n",
" print(question)\n",
" source = question_router.invoke({\"question\": question}) \n",
" source = question_router.invoke({\"question\": question})\n",
" print(source)\n",
" print(source['datasource'])\n",
" if source['datasource'] == 'web_search':\n",
Expand Down Expand Up @@ -648,7 +649,7 @@
"source": [
"Trace: \n",
"\n",
"https://smith.langchain.com/public/8d449b67-6bc4-4ecf-9153-759cd21df24f/r"
"https://smith.langchain.com/public/bbdef8eb-0c42-4df6-9eaa-3e7e75c87df9/r"
]
},
{
Expand All @@ -660,7 +661,6 @@
"source": [
"# Compile\n",
"app = workflow.compile()\n",
"\n",
"# Test\n",
"from pprint import pprint\n",
"inputs = {\"question\": \"Who are the Bears expected to draft first in the NFL draft?\"}\n",
Expand All @@ -677,13 +677,13 @@
"source": [
"Trace: \n",
"\n",
"https://smith.langchain.com/public/c785f9c0-f519-4a38-ad5a-febb59a2139c/r"
"https://smith.langchain.com/public/0e6378e7-b8d4-4979-8357-05f854584cc6/r"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1059b3e-d197-47fc-b55f-82a3406016f3",
"id": "337d5b89-7030-4ae9-85ed-f5f9f6da0c26",
"metadata": {},
"outputs": [],
"source": []
Expand All @@ -705,7 +705,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
"version": "3.12.7"
}
},
"nbformat": 4,
Expand Down
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